Theme & Design

Introduction

There are several advantages to dedicate a data visualization portfolio and 10-week effort to digital currency:

• Massive and Robust data source: Blockchain is a public transaction log that exists as a distributed database, validated by its powerful algorithm.

• Fitting data types: Blockchain data includes time and IP addresses, and can be updated frequently, which means it is useful for both the static and dynamic data visualizations.

• Well-supported: I can save the entire blockchain file locally,use community-maintained tools to parse it, and keep the data up-to-date.

• Policy-setting: Digital currencies are being used as store of value to fight inflation in turbulent economies such as Zimbabwe and Latin American countries. Central Banks are issuing experimental policies in response to the dessemination of digital currency and blockchain technology.

This paper will explore some data sources published by major Bitcoin exchanges and platforms as an market overview.

# ISSUE: what is target user's viewing device and optimum dpi?
# ISSUE: Should probably change x tick label to lighter grey.
rectangle <- data.frame(xmin = as.POSIXct(c("2017-03-25")),
                        xmax = as.POSIXct(c("2017-10-21")),
                        ymin = -Inf,
                        ymax = Inf)

line <- ggplot(data=btc_price) +
  geom_line(size=.25,
            aes(Date, Value),
            color="#325a8c") +
  geom_vline(xintercept = as.POSIXct("2017-03-25"),
             colour="#ff7575",
             size=.5,
             linetype="dotted",
             alpha=.75) +
  scale_y_continuous(name ="Bitcoin Market Price in USD",
                     breaks = (seq(0,6000,1000))
                     ) +
  scale_x_datetime(date_breaks = "1 year",
                   labels = seq(2009,2018,1),
                   expand=c(0,0)) +
  ggtitle("Bitcoin Price",
          subtitle = "experienced the greatest surge in history this year") +
  labs(caption = "Source: Blockchain.com") +
  annotate(geom="text",
           x=as.POSIXct("2016-01-15"),
           y=2200,
           label="Surge started on Mar 25, 2017",
           colour="#ff7575",
           family="Avenir",
           fontface="bold",
           alpha=1) +
  theme_jiye

line + geom_rect(data = rectangle, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
                 fill = "#ff7575", alpha = 0.1)

Bitcoin price is volatile

The price of bitcoin can increase or decrease drastically over a short period of time. This graph mainly serves as a priliminary evidence to pinpoint certain time points where a new policy or event may have impacted the price of Bitcoin.

# graph 2-1: nodes map
# map theme
theme_map <- theme(panel.grid.major.y = element_blank(),
                    panel.grid.major.x = element_blank(),
                    panel.background = element_blank(),
                    plot.title = element_text(size=24,
                                              family = "Helvetica",
                                              colour = "#3a3a3a",
                                              face = "bold"),
                    plot.subtitle = element_text(size=12,
                                                 family = "Avenir",
                                                 colour = "#666666"),
                    axis.title.y = element_blank(),
                    axis.title.x = element_blank(),
                    axis.text.y = element_blank(),
                    axis.text.x = element_blank(),
                    axis.ticks.y = element_blank(),
                                        axis.ticks.x = element_blank(),
                    legend.position = "none",
                    plot.caption = element_text(size=8,
                                                family = "Avenir",
                                                colour = "#666666",
                                                hjust = 0
                    ),
                    plot.margin = unit(c(1,0,0,0), "cm"))

# ISSUE: How to color code by country's node count?
# ISSUE: The map projection method is visibly elongated for tropical areas.
# ISSUE: (RESOLVED)How to remove Antarctica? 
mp <- NULL
mapWorld <- borders("world", colour="#FFFFFF", fill='grey75', alpha=0.6,
                    ylim = c(-50, 70)) # create a layer of borders
mp <- ggplot() + mapWorld

# Now Layer the cities on top
mp <- mp+ geom_point(aes(x=nodes$longitude, y=nodes$latitude) ,color="#fe5346", size=0.75, alpha=.25) + 
  geom_path(aes(x=nodes$longitude, y=nodes$latitude), color="#FF9900", size=.1, alpha=0.025,
            linejoin = "bevel") +
  ggtitle("Global Nodes Distribution",
          subtitle = "is uneven across continents") +
  labs(caption = "Source: BitNodes API") +
  theme_map
mp

# graph 2-2: waffle chart
nation_cols <- c(
                "#93B7BE","#584F50","#b3906a","#FF991C",'#fb4d3d',
                '#ff9b9b','#313E50','#78323e','#20FC8F','#90E0F3',
                '#5b8e7d','#ffb7c5','#CFB53B','#769855','#e13224',
                "#CCCCCC"
                )

waffle <- waffle(nation_list%/%100,
       rows=3,
       size=1.5,
       colors=nation_cols,
       legend_pos = "bottom",
       xlab="Total nodes as of Oct 18, 2017 (10320)",
       title='Bitcoin Nodes Distribution by Countries (source: BitNodes API)')
waffle

# Alternative graph 2-2: percentage bar
# graph 2 alt: stacked percentage bar

#locks factor level
top_country_nodes$country <- factor(top_country_nodes$country, levels = top_country_nodes$country)

# ISSUE: Hard coded number, should fix soon.
# ISSUE: Ugly legend or insufficient legend?
# ISSUE: don't know how to right-ligh axis tick label.
nation_cols2 <- c(
                "#93B7BE","#584F50","#b3906a","#FF991C",'#fb4d3d',
                "#999999","#999999","#999999","#999999","#999999",
                "#999999","#999999","#999999","#999999","#999999",
                "#dddddd"
                )
perc_bar <- ggplot(data = top_country_nodes, aes(x="", y = node, fill=factor(country))) + 
  geom_bar(stat='identity', position = position_stack(reverse = TRUE), width=.75, colour="white") +
  coord_flip() +
  scale_fill_manual(values=nation_cols2) +
  theme(panel.grid.major.y =element_blank(),
                    aspect.ratio = 0.05,
                    panel.grid.major.x = element_blank(),
                    panel.background = element_blank(),
                    plot.title = element_text(size=24,
                                              family = "Helvetica",
                                              colour = "#3a3a3a",
                                              face = "bold"),
                    plot.subtitle = element_text(size=12,
                                                 family = "Avenir",
                                                 colour = "#666666"),
                    #axis.title.y = element_text(colour="#325a8c"),
                    axis.title.y=element_blank(),            
                    axis.title.x = element_blank(),
                    axis.text.x = element_text(colour="#325a8c", family="Avenir",hjust =1),
                    #axis.text.x = element_blank(),
                    axis.ticks.y = element_blank(),
                    #axis.ticks.x = element_blank(),
                    legend.position = "none",
                    axis.line.x = element_line(),
                    #legend.text.align = ,
                    #legend.title = element_blank(),
                    #legend.text = element_text(family = "Avenir", size = 10),
                    plot.caption = element_text(size=8,
                                                family = "Avenir",
                                                colour = "#666666",
                                                hjust = 0
                                                ),
                    plot.margin = unit(c(0,2,0,1), "cm"))
perc_bar + scale_y_continuous(name="",labels=c("0%", "USA\n29.62%", "Germany\n17.16%", "France\n6.65%", "Netherlands\n5.19%", "China\n5.11%", "< Countries with over 100 nodes", "Rest of the World\n13.97%"),
                     breaks = (c(0, 2962, (2962+1716), (2962+1716+665), (2962+1716+665+519), (2962+1716+665+519+511),9973-1397,
                              sum(top_country_nodes$node))),
                     expand=c(0,0)
                     ) +
                     labs(caption = "Source: BitNodes") +
  ggtitle("Blockchain Network is Distributed",
          subtitle = " in high GDP countries, with the Middle East underrepresented")

(ISSUE: REWRITE THIS) Nodes, nodes-nodes, Nodes?

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# note: color #85bb65 is nicknamed as "dollar bill green".
#       color #FF9900 is the orange from bitcoin logo.
# ISSUE:Both colors are ugly af, switch soon.
area <- ggplot(data=agg, aes(x = as.POSIXct(date), y = volume, fill = as.factor(unit))) +
  geom_area(position = "stack") +
  scale_x_datetime(date_breaks = "3 months", expand=c(0,0)) +
  scale_y_continuous(breaks = c(0, 250, 500, 750, 1000, 2000, 3000)) + 
  scale_fill_manual(values = alpha(c("#FF9900", "#85bb65")), 0.2) +
  ggtitle("Bitcoin Daily Trading Volume",
          subtitle = "in number of bitcoin & Thousands of USD") +
  labs(caption = "Source: Coinbase API",
       x = "",
       y = "Volume") +
  theme(panel.grid.major.y =element_line( size=.1, color="#999999"),
                    panel.grid.major.x = element_blank(),
                    panel.background = element_blank(),
                    plot.title = element_text(size=24,
                                              family = "Helvetica",
                                              colour = "#3a3a3a",
                                              face = "bold"),
                    plot.subtitle = element_text(size=12,
                                                 family = "Avenir",
                                                 colour = "#666666"),
                    #axis.title.y = element_text(colour="#325a8c"),
                    axis.title.x = element_blank(),
                    #axis.text.y = element_text(colour="#325a8c"),
#                    axis.text.x = element_blank(),
                    axis.ticks.y = element_blank(),
#                    axis.ticks.x = element_blank(),
                    legend.position = "none",
                    plot.caption = element_text(size=8,
                                                family = "Avenir",
                                                colour = "#666666",
                                                hjust = 0
                                                ),
                    plot.margin = unit(c(0,0,0,0), "cm"))

area

(ISSUE: Rewrite) High Trading Volume Volatility:

(Should discuss how the price peaks usually precedes bitcoin peaks, discuss irational buying behavior, market optimism, etc.) In this graph, we can see that bitcoin’s daily trading volume is extremely volatile, perhaps even more so than its price. This level of volatility can be a sign for market manipulation, especially given Bitcoin’s young economy, novel nature, and lack of regulation. Together with the price trend, this graph can help identify major policy changes that either encourage (spike) or discourage (trough) speculation.

# View in full screen for clarity.
# ISSUE: Probably shouldn't use Heatmap.
# ISSUE: Needs more deliberate, consistent color scheme
heatmap <- ggplot(soc, aes(x=rater, y=ratee, fill=score)) + geom_tile() +
  scale_fill_distiller(type="div", palette = "RdYlBu") +
  ggtitle("The trust nework of Bitcoin traders",
          subtitle = "is sparse and unregulated") +
  labs(caption = "Source: Bitcoin OTC trust network by Stanford University") +
  theme(panel.grid.major.y = element_blank(),
        aspect.ratio = 1,
                    panel.grid.major.x = element_blank(),
                    panel.background = element_blank(),
                    plot.title = element_text(size=24,
                                              family = "Helvetica",
                                              colour = "#3a3a3a",
                                              face = "bold"),
                    plot.subtitle = element_text(size=12,
                                                 family = "Avenir",
                                                 colour = "#666666"),
                    #axis.title.y = element_text(colour="#325a8c"),
                    axis.title.x = element_blank(),
                    #axis.text.y = element_text(colour="#325a8c"),
#                    axis.text.x = element_blank(),
                    axis.ticks.y = element_blank(),
#                    axis.ticks.x = element_blank(),
                    legend.position = "none",
                    plot.caption = element_text(size=8,
                                                family = "Avenir",
                                                colour = "#666666",
                                                hjust = 0
                                                ),
                    plot.margin = unit(c(0,0,0,0), "cm"))
heatmap

The Who-trust-whom Network:

While sparse and barely visible, this graph does provide some insights about the Bitcoin market. First, it allows us to identify

some super raters and ratees, who are more likely to be the major player capable of manipulating Bitcoin price. Second, although orange (trustworthy) is more prevalent, there are quite a few blue spots on this heatmap, marking the users that are rated as fraud by others. At a glimpse, fraud is frequent in digital currency market, which underscores its unregulated nature.